Yes, you can calculate a standard SimScore report first and then make adjustments to it based on token weighting. Here’s a general approach on how to do this:

Steps to Adjust a Standard SimScore Report with Token Weighting

1. Calculate the Standard SimScore Report:
   • First, generate the standard SimScore report by processing the opinions without considering token weighting. This involves vectorizing the text, calculating a centroid, and determining similarities.
2. Apply Token Weight Adjustments:
   • Adjust the results from the standard report based on the token weights. This adjustment will involve recalculating the centroid and similarities, factoring in the token weights.
3. Update the SimScore Graph:
   • Finally, update the SimScore graph to reflect the adjusted results.

Detailed Approach

1. Calculate the Standard SimScore Report

• Vectorize Opinions: Convert each written opinion into a numerical vector.
• Calculate Centroid: Compute the centroid by averaging the vectors.
• Calculate Similarities: Measure how similar each opinion is to the centroid.

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Example function to convert text to vector
def text_to_vector(text):
    # Placeholder vectorization method
    return np.random.rand(300)

# Sample opinions
opinions = ["Leadership is crucial.", "Collaboration is essential.", "Innovation drives success."]

# Convert opinions to vectors
vectors = [text_to_vector(opinion) for opinion in opinions]

# Calculate the standard centroid (unweighted)
centroid_standard = np.mean(vectors, axis=0)

# Calculate similarities to the standard centroid
similarities_standard = [cosine_similarity([centroid_standard], [vec])[0][0] for vec in vectors]

print("Standard Centroid:", centroid_standard)
print("Standard Similarities:", similarities_standard)

2. Apply Token Weight Adjustments

After calculating the standard centroid and similarities, you’ll adjust these by incorporating token weights.

• Recalculate Centroid with Token Weights:
  • Use the token weights to calculate a new centroid.
• Adjust Similarities:
  • Recalculate the similarities using the new weighted centroid or directly adjust the original similarities by multiplying them with the respective token weights.

# Sample token weights for the opinions
token_weights = [10, 20, 30]  # Corresponding to the three opinions

# Recalculate the weighted centroid
weighted_sum = np.sum([token_weights[i] * vectors[i] for i in range(len(vectors))], axis=0)
total_tokens = sum(token_weights)
centroid_weighted = weighted_sum / total_tokens

# Recalculate similarities to the weighted centroid
similarities_weighted = [cosine_similarity([centroid_weighted], [vec])[0][0] for vec in vectors]

# Optionally adjust original similarities directly by token weights
adjusted_similarities = [similarities_standard[i] * token_weights[i] for i in range(len(similarities_standard))]

print("Weighted Centroid:", centroid_weighted)
print("Weighted Similarities:", similarities_weighted)
print("Adjusted Standard Similarities:", adjusted_similarities)

3. Update the SimScore Graph

• Graph Update:
  • Create or update the graph based on the newly calculated similarities. Nodes represent opinions, and edges represent the adjusted similarity scores.

# Function to update or create a SimScore graph (simplified)
def update_simscore_graph(opinions, similarities):
    graph = {}
    for i, opinion in enumerate(opinions):
        graph[opinion] = {
            "similarity": similarities[i]
        }
    return graph

# Generate the graph with adjusted similarities
simscore_graph = update_simscore_graph(opinions, adjusted_similarities)

print("Updated SimScore Graph:", simscore_graph)

Summary